Prevalence and socio-structural determinants of tobacco exposure in young women: Data from the Healthy Trajectories Initiative (HeLTI) study in urban Soweto, South Africa
Bibliographic record
Abstract
BACKGROUND: Tobacco use is a major public health risk worldwide, which has increased on the African continent over the past 40 years. Socio-economic factors impact tobacco use and exposure, but little is known about the scope of this problem in young women living in an urban, historically disadvantaged township in contemporary South Africa. This study aimed to identify the prevalence of tobacco use in a cohort of young South African women using serum cotinine, and to assess the association between a number of socio-economic and social factors and tobacco use in this setting. METHODS: Secondary analysis was conducted on cross-sectional data from the Healthy Life Trajectories Initiative (HeLTI) study. Serum cotinine was measured and a cut-off of ≥ 10 ng/mL was classified as tobacco use. Household socio-economic, socio-demographic and health information were collected by an interviewer-administered questionnaire. RESULTS: Cotinine data was available for 1508 participants, of whom 29.2% (n = 441) had cotinine levels indicative of tobacco use. In regression analyses, moderate to severe socio-economic vulnerability (score 2-3 OR 1.66, p = 0.008; score ≥4: OR 1.63, p = 0.026) and multiparity (OR 1.74, p = 0.013) were associated with tobacco use. In addition, alcohol dependence (OR 3.07, p < 0.001) and drug use (OR 4.84, p < 0.001) were associated with tobacco use. CONCLUSION: Young women with multiple children, moderate to severe socio-economic vulnerability, and alcohol and drug use were identified as more likely to use tobacco, indicating the need for targeted anti-tobacco interventions to curb the impact of tobacco on the growing burden of noncommunicable diseases in this setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".